Overview

Dataset statistics

Number of variables15
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory121.9 B

Variable types

NUM15

Reproduction

Analysis started2020-08-25 00:01:55.552350
Analysis finished2020-08-25 00:02:26.171422
Duration30.62 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Moorehead00 is highly correlated with Nader00 and 1 other fieldsHigh correlation
Nader00 is highly correlated with Moorehead00 and 1 other fieldsHigh correlation
Phillips00 is highly correlated with Harris00High correlation
Harris00 is highly correlated with Phillips00High correlation
Total00 is highly correlated with Bush00 and 5 other fieldsHigh correlation
Bush00 is highly correlated with Total00 and 4 other fieldsHigh correlation
Clinton96 is highly correlated with Bush00 and 5 other fieldsHigh correlation
Dole96 is highly correlated with Bush00 and 5 other fieldsHigh correlation
Perot96 is highly correlated with Nader00 and 6 other fieldsHigh correlation
Total96 is highly correlated with Bush00 and 5 other fieldsHigh correlation
target is highly correlated with Bush00 and 5 other fieldsHigh correlation
Bush00 has unique values Unique
Total00 has unique values Unique
Clinton96 has unique values Unique
Dole96 has unique values Unique
Perot96 has unique values Unique
Total96 has unique values Unique
target has unique values Unique
Hagelin00 has 3 (4.5%) zeros Zeros
Harris00 has 14 (20.9%) zeros Zeros
McReynolds00 has 13 (19.4%) zeros Zeros
Moorehead00 has 3 (4.5%) zeros Zeros
Phillips00 has 4 (6.0%) zeros Zeros

Variables

Bush00
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43355.76119402985
Minimum1316.0
Maximum289456.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:26.217802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1316
5-th percentile2204.9
Q14746.5
median20196
Q356541.5
95-th percentile169730.7
Maximum289456
Range288140
Interquartile range (IQR)51795

Descriptive statistics

Standard deviation56989.41863
Coefficient of variation (CV)1.31446011
Kurtosis5.005719698
Mean43355.76119
Median Absolute Deviation (MAD)17158
Skewness2.10679033
Sum2904836
Variance3247793836
2020-08-25T00:02:26.330899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
7529311.5%
 
1605911.5%
 
15284611.5%
 
498311.5%
 
913811.5%
 
3624811.5%
 
3541911.5%
 
2862711.5%
 
7302911.5%
 
405111.5%
 
248111.5%
 
3949711.5%
 
498511.5%
 
287311.5%
 
6042611.5%
 
17696711.5%
 
9010111.5%
 
3064611.5%
 
2019611.5%
 
4174511.5%
 
28945611.5%
 
1217611.5%
 
232611.5%
 
131611.5%
 
11518511.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
131611.5%
 
166911.5%
 
184011.5%
 
215311.5%
 
232611.5%
 
244811.5%
 
248111.5%
 
269811.5%
 
287311.5%
 
303811.5%
 
ValueCountFrequency (%) 
28945611.5%
 
18431211.5%
 
17727911.5%
 
17696711.5%
 
15284611.5%
 
15208211.5%
 
13447611.5%
 
11518511.5%
 
10614111.5%
 
9010111.5%
 

Buchanan00
Real number (ℝ≥0)

Distinct count59
Unique (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.46268656716416
Minimum9.0
Maximum3407.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:26.447523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24.6
Q146.5
median114
Q3285.5
95-th percentile747.3
Maximum3407
Range3398
Interquartile range (IQR)239

Descriptive statistics

Standard deviation449.3095824
Coefficient of variation (CV)1.738392448
Kurtosis37.13394966
Mean258.4626866
Median Absolute Deviation (MAD)85
Skewness5.491756879
Sum17317
Variance201879.1009
2020-08-25T00:02:26.559549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2946.0%
 
57023.0%
 
30523.0%
 
9023.0%
 
3923.0%
 
10823.0%
 
65011.5%
 
24211.5%
 
2211.5%
 
3011.5%
 
2411.5%
 
7111.5%
 
911.5%
 
3311.5%
 
8311.5%
 
50411.5%
 
340711.5%
 
83611.5%
 
3611.5%
 
56111.5%
 
8911.5%
 
12211.5%
 
18611.5%
 
27011.5%
 
18211.5%
 
Other values (34)3450.7%
 
ValueCountFrequency (%) 
911.5%
 
1011.5%
 
2211.5%
 
2411.5%
 
2611.5%
 
2711.5%
 
2946.0%
 
3011.5%
 
3311.5%
 
3611.5%
 
ValueCountFrequency (%) 
340711.5%
 
101011.5%
 
83611.5%
 
78911.5%
 
65011.5%
 
57023.0%
 
56311.5%
 
56111.5%
 
53811.5%
 
50411.5%
 

Nader00
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count65
Unique (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1441.2388059701493
Minimum19.0
Maximum9986.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:26.673981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile39
Q195
median562
Q31871
95-th percentile5501.3
Maximum9986
Range9967
Interquartile range (IQR)1776

Descriptive statistics

Standard deviation2021.344018
Coefficient of variation (CV)1.402504574
Kurtosis5.221706505
Mean1441.238806
Median Absolute Deviation (MAD)509
Skewness2.181436328
Sum96563
Variance4085831.639
2020-08-25T00:02:26.778124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3923.0%
 
7523.0%
 
388111.5%
 
7611.5%
 
10311.5%
 
40911.5%
 
9111.5%
 
95011.5%
 
13811.5%
 
2611.5%
 
5611.5%
 
193211.5%
 
28411.5%
 
1911.5%
 
5411.5%
 
181011.5%
 
8611.5%
 
9711.5%
 
25511.5%
 
13911.5%
 
8511.5%
 
43511.5%
 
275211.5%
 
15711.5%
 
25811.5%
 
Other values (40)4059.7%
 
ValueCountFrequency (%) 
1911.5%
 
2611.5%
 
2911.5%
 
3923.0%
 
5311.5%
 
5411.5%
 
5611.5%
 
5911.5%
 
7523.0%
 
7611.5%
 
ValueCountFrequency (%) 
998611.5%
 
734811.5%
 
709911.5%
 
556411.5%
 
535511.5%
 
447011.5%
 
406611.5%
 
388111.5%
 
358711.5%
 
339211.5%
 

Browne00
Real number (ℝ≥0)

Distinct count58
Unique (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.7164179104478
Minimum4.0
Maximum3211.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:27.053548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q123.5
median116
Q3321.5
95-th percentile1059
Maximum3211
Range3207
Interquartile range (IQR)298

Descriptive statistics

Standard deviation476.3952334
Coefficient of variation (CV)1.697069366
Kurtosis21.45594947
Mean280.7164179
Median Absolute Deviation (MAD)98
Skewness4.002837184
Sum18808
Variance226952.4184
2020-08-25T00:02:27.162903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1734.5%
 
1823.0%
 
5323.0%
 
1123.0%
 
12723.0%
 
2123.0%
 
1223.0%
 
3223.0%
 
321111.5%
 
29711.5%
 
110411.5%
 
5111.5%
 
11611.5%
 
5211.5%
 
2411.5%
 
6011.5%
 
2311.5%
 
95411.5%
 
4011.5%
 
75911.5%
 
18511.5%
 
20411.5%
 
19411.5%
 
1011.5%
 
121211.5%
 
Other values (33)3349.3%
 
ValueCountFrequency (%) 
411.5%
 
611.5%
 
1011.5%
 
1123.0%
 
1223.0%
 
1311.5%
 
1411.5%
 
1734.5%
 
1823.0%
 
2123.0%
 
ValueCountFrequency (%) 
321111.5%
 
122211.5%
 
121211.5%
 
110411.5%
 
95411.5%
 
89211.5%
 
75911.5%
 
74311.5%
 
65811.5%
 
64311.5%
 

Hagelin00
Real number (ℝ≥0)

ZEROS

Distinct count38
Unique (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.1044776119403
Minimum0.0
Maximum444.0
Zeros3
Zeros (%)4.5%
Memory size664.0 B
2020-08-25T00:02:27.281238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median13
Q333.5
95-th percentile138.5
Maximum444
Range444
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation66.16580054
Coefficient of variation (CV)1.940091307
Kurtosis22.72563967
Mean34.10447761
Median Absolute Deviation (MAD)11
Skewness4.257118995
Sum2285
Variance4377.913161
2020-08-25T00:02:27.379189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2710.4%
 
369.0%
 
457.5%
 
157.5%
 
2634.5%
 
034.5%
 
734.5%
 
1323.0%
 
1123.0%
 
1523.0%
 
1423.0%
 
11911.5%
 
2411.5%
 
16011.5%
 
2011.5%
 
3411.5%
 
12811.5%
 
3911.5%
 
1811.5%
 
1611.5%
 
21511.5%
 
3311.5%
 
14311.5%
 
3811.5%
 
9411.5%
 
Other values (13)1319.4%
 
ValueCountFrequency (%) 
034.5%
 
157.5%
 
2710.4%
 
369.0%
 
457.5%
 
734.5%
 
811.5%
 
1123.0%
 
1211.5%
 
1323.0%
 
ValueCountFrequency (%) 
44411.5%
 
21511.5%
 
16011.5%
 
14311.5%
 
12811.5%
 
11911.5%
 
9411.5%
 
8211.5%
 
8111.5%
 
6511.5%
 

Harris00
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count24
Unique (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.28358208955223
Minimum0.0
Maximum9888.0
Zeros14
Zeros (%)20.9%
Memory size664.0 B
2020-08-25T00:02:27.489893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38.5
95-th percentile43.5
Maximum9888
Range9888
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation1207.028693
Coefficient of variation (CV)7.7233237
Kurtosis66.97761007
Mean156.2835821
Median Absolute Deviation (MAD)3
Skewness8.183337758
Sum10471
Variance1456918.267
2020-08-25T00:02:27.589429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01420.9%
 
11014.9%
 
469.0%
 
269.0%
 
546.0%
 
634.5%
 
334.5%
 
823.0%
 
1123.0%
 
1323.0%
 
723.0%
 
1411.5%
 
1011.5%
 
4011.5%
 
1811.5%
 
4911.5%
 
4511.5%
 
8811.5%
 
911.5%
 
3611.5%
 
3811.5%
 
3511.5%
 
3011.5%
 
988811.5%
 
ValueCountFrequency (%) 
01420.9%
 
11014.9%
 
269.0%
 
334.5%
 
469.0%
 
546.0%
 
634.5%
 
723.0%
 
823.0%
 
911.5%
 
ValueCountFrequency (%) 
988811.5%
 
8811.5%
 
4911.5%
 
4511.5%
 
4011.5%
 
3811.5%
 
3611.5%
 
3511.5%
 
3011.5%
 
1811.5%
 

McReynolds00
Real number (ℝ≥0)

ZEROS

Distinct count20
Unique (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.029850746268657
Minimum0.0
Maximum658.0
Zeros13
Zeros (%)19.4%
Memory size664.0 B
2020-08-25T00:02:27.703492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile33.2
Maximum658
Range658
Interquartile range (IQR)4

Descriptive statistics

Standard deviation87.4968346
Coefficient of variation (CV)4.597872877
Kurtosis45.8706861
Mean19.02985075
Median Absolute Deviation (MAD)2
Skewness6.604572654
Sum1275
Variance7655.696065
2020-08-25T00:02:27.812920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01319.4%
 
11217.9%
 
31116.4%
 
2710.4%
 
557.5%
 
446.0%
 
723.0%
 
1011.5%
 
3611.5%
 
1111.5%
 
3511.5%
 
1611.5%
 
2711.5%
 
911.5%
 
2911.5%
 
611.5%
 
811.5%
 
30211.5%
 
1411.5%
 
65811.5%
 
ValueCountFrequency (%) 
01319.4%
 
11217.9%
 
2710.4%
 
31116.4%
 
446.0%
 
557.5%
 
611.5%
 
723.0%
 
811.5%
 
911.5%
 
ValueCountFrequency (%) 
65811.5%
 
30211.5%
 
3611.5%
 
3511.5%
 
2911.5%
 
2711.5%
 
1611.5%
 
1411.5%
 
1111.5%
 
1011.5%
 

Moorehead00
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count40
Unique (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.44776119402985
Minimum0.0
Maximum167.0
Zeros3
Zeros (%)4.5%
Memory size664.0 B
2020-08-25T00:02:27.933135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median12
Q332
95-th percentile117
Maximum167
Range167
Interquartile range (IQR)28

Descriptive statistics

Standard deviation37.4049436
Coefficient of variation (CV)1.362768473
Kurtosis4.126770533
Mean27.44776119
Median Absolute Deviation (MAD)10
Skewness2.100731594
Sum1839
Variance1399.129806
2020-08-25T00:02:28.048361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
269.0%
 
357.5%
 
1257.5%
 
546.0%
 
734.5%
 
034.5%
 
434.5%
 
123.0%
 
2023.0%
 
2923.0%
 
923.0%
 
5923.0%
 
15011.5%
 
12411.5%
 
2811.5%
 
4111.5%
 
12311.5%
 
7611.5%
 
2211.5%
 
3711.5%
 
1811.5%
 
1011.5%
 
611.5%
 
1411.5%
 
1711.5%
 
Other values (15)1522.4%
 
ValueCountFrequency (%) 
034.5%
 
123.0%
 
269.0%
 
357.5%
 
434.5%
 
546.0%
 
611.5%
 
734.5%
 
923.0%
 
1011.5%
 
ValueCountFrequency (%) 
16711.5%
 
15011.5%
 
12411.5%
 
12311.5%
 
10311.5%
 
9611.5%
 
7711.5%
 
7611.5%
 
7011.5%
 
5923.0%
 

Phillips00
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count36
Unique (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.82089552238806
Minimum0.0
Maximum2927.0
Zeros4
Zeros (%)6.0%
Memory size664.0 B
2020-08-25T00:02:28.162900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q13
median10
Q320.5
95-th percentile73.4
Maximum2927
Range2927
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation356.4125514
Coefficient of variation (CV)5.58457459
Kurtosis65.96750071
Mean63.82089552
Median Absolute Deviation (MAD)8
Skewness8.094042799
Sum4276
Variance127029.9068
2020-08-25T00:02:28.274238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3811.9%
 
1057.5%
 
257.5%
 
046.0%
 
834.5%
 
134.5%
 
734.5%
 
1934.5%
 
634.5%
 
1323.0%
 
1823.0%
 
923.0%
 
6611.5%
 
6911.5%
 
5711.5%
 
11011.5%
 
7411.5%
 
7211.5%
 
511.5%
 
292711.5%
 
411.5%
 
2111.5%
 
3411.5%
 
1611.5%
 
2211.5%
 
Other values (11)1116.4%
 
ValueCountFrequency (%) 
046.0%
 
134.5%
 
257.5%
 
3811.9%
 
411.5%
 
511.5%
 
634.5%
 
734.5%
 
834.5%
 
923.0%
 
ValueCountFrequency (%) 
292711.5%
 
18811.5%
 
11011.5%
 
7411.5%
 
7211.5%
 
7011.5%
 
6911.5%
 
6611.5%
 
5711.5%
 
4611.5%
 

Total00
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88978.07462686567
Minimum2403.0
Maximum625269.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:28.388337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2403
5-th percentile3847.7
Q18079.5
median34941
Q3102873.5
95-th percentile383855.9
Maximum625269
Range622866
Interquartile range (IQR)94794

Descriptive statistics

Standard deviation131680.7808
Coefficient of variation (CV)1.479923918
Kurtosis6.223965466
Mean88978.07463
Median Absolute Deviation (MAD)29546
Skewness2.443332058
Sum5961531
Variance1.733982804e+10
2020-08-25T00:02:28.515958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13704411.5%
 
5736011.5%
 
43228611.5%
 
10263411.5%
 
985411.5%
 
858311.5%
 
1850811.5%
 
6521911.5%
 
19823011.5%
 
5028511.5%
 
6067411.5%
 
1246111.5%
 
6688511.5%
 
623411.5%
 
1630011.5%
 
1273011.5%
 
5715411.5%
 
334211.5%
 
14271311.5%
 
2620511.5%
 
39693811.5%
 
539511.5%
 
680111.5%
 
8624211.5%
 
7798911.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
240311.5%
 
250411.5%
 
334211.5%
 
379411.5%
 
397311.5%
 
463411.5%
 
466611.5%
 
517411.5%
 
539511.5%
 
564211.5%
 
ValueCountFrequency (%) 
62526911.5%
 
57330611.5%
 
43228611.5%
 
39693811.5%
 
35333111.5%
 
27998111.5%
 
26442811.5%
 
21839511.5%
 
19823011.5%
 
18437711.5%
 

Clinton96
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37939.82089552239
Minimum829.0
Maximum320736.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:28.635562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum829
5-th percentile1590.3
Q13136.5
median13246
Q338956
95-th percentile172576.5
Maximum320736
Range319907
Interquartile range (IQR)35819.5

Descriptive statistics

Standard deviation66019.19075
Coefficient of variation (CV)1.740102857
Kurtosis9.585589583
Mean37939.8209
Median Absolute Deviation (MAD)10936
Skewness3.012806777
Sum2541968
Variance4358533547
2020-08-25T00:02:28.743686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14422311.5%
 
940511.5%
 
2187011.5%
 
669111.5%
 
4183511.5%
 
2085111.5%
 
358311.5%
 
2204211.5%
 
279111.5%
 
2852011.5%
 
209511.5%
 
666511.5%
 
227311.5%
 
2712111.5%
 
4505111.5%
 
2575011.5%
 
3776811.5%
 
5005811.5%
 
11225811.5%
 
299211.5%
 
6364811.5%
 
138811.5%
 
482411.5%
 
1643411.5%
 
86811.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
82911.5%
 
86811.5%
 
138811.5%
 
153011.5%
 
173111.5%
 
173411.5%
 
179411.5%
 
198511.5%
 
209511.5%
 
227311.5%
 
ValueCountFrequency (%) 
32073611.5%
 
31737811.5%
 
23062111.5%
 
18472811.5%
 
14422311.5%
 
11225811.5%
 
10551311.5%
 
8041611.5%
 
7890511.5%
 
6673511.5%
 

Dole96
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33482.44776119403
Minimum913.0
Maximum209634.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:28.855406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum913
5-th percentile1434
Q13343.5
median15608
Q341993.5
95-th percentile135763.3
Maximum209634
Range208721
Interquartile range (IQR)38650

Descriptive statistics

Standard deviation44743.05879
Coefficient of variation (CV)1.336313854
Kurtosis3.759338354
Mean33482.44776
Median Absolute Deviation (MAD)13284
Skewness1.970189826
Sum2243324
Variance2001941310
2020-08-25T00:02:28.958830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10602611.5%
 
3508911.5%
 
4405911.5%
 
5977811.5%
 
341511.5%
 
2851611.5%
 
381311.5%
 
2270911.5%
 
4259011.5%
 
163611.5%
 
318811.5%
 
3391411.5%
 
596011.5%
 
429911.5%
 
978111.5%
 
12685711.5%
 
13376211.5%
 
2624411.5%
 
91311.5%
 
8798011.5%
 
116611.5%
 
324811.5%
 
1560811.5%
 
2011411.5%
 
20963411.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
91311.5%
 
116611.5%
 
136111.5%
 
139811.5%
 
151811.5%
 
156311.5%
 
163611.5%
 
171711.5%
 
185111.5%
 
193911.5%
 
ValueCountFrequency (%) 
20963411.5%
 
15212511.5%
 
14283411.5%
 
13662111.5%
 
13376211.5%
 
12685711.5%
 
10602611.5%
 
8798011.5%
 
8088211.5%
 
6919811.5%
 

Perot96
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7220.537313432836
Minimum316.0
Maximum38964.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:29.068834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum316
5-th percentile411.7
Q11072.5
median3739
Q38700
95-th percentile25220.5
Maximum38964
Range38648
Interquartile range (IQR)7627.5

Descriptive statistics

Standard deviation8972.961339
Coefficient of variation (CV)1.242699947
Kurtosis3.307798453
Mean7220.537313
Median Absolute Deviation (MAD)2933
Skewness1.88501286
Sum483776
Variance80514035.19
2020-08-25T00:02:29.179760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1731911.5%
 
727211.5%
 
114011.5%
 
187411.5%
 
327211.5%
 
166611.5%
 
543211.5%
 
858711.5%
 
57811.5%
 
37611.5%
 
177411.5%
 
667211.5%
 
31611.5%
 
39311.5%
 
160211.5%
 
120811.5%
 
85111.5%
 
2524911.5%
 
40611.5%
 
105411.5%
 
52111.5%
 
84111.5%
 
93811.5%
 
2472211.5%
 
87811.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
31611.5%
 
37611.5%
 
39311.5%
 
40611.5%
 
42511.5%
 
52111.5%
 
57811.5%
 
63011.5%
 
65211.5%
 
66711.5%
 
ValueCountFrequency (%) 
3896411.5%
 
3699011.5%
 
3073911.5%
 
2524911.5%
 
2515411.5%
 
2472211.5%
 
1838911.5%
 
1819111.5%
 
1801111.5%
 
1731911.5%
 

Total96
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79129.13432835821
Minimum2164.0
Maximum553491.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:29.303521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2164
5-th percentile3524.4
Q17672
median33699
Q390915.5
95-th percentile355809.6
Maximum553491
Range551327
Interquartile range (IQR)83243.5

Descriptive statistics

Standard deviation117817.1154
Coefficient of variation (CV)1.488922082
Kurtosis6.07066566
Mean79129.13433
Median Absolute Deviation (MAD)28891
Skewness2.438399588
Sum5301652
Variance1.388087268e+10
2020-08-25T00:02:29.409798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2115911.5%
 
6296311.5%
 
5464611.5%
 
10768711.5%
 
37621811.5%
 
680111.5%
 
1632611.5%
 
4703511.5%
 
7391111.5%
 
6301411.5%
 
9168511.5%
 
4396311.5%
 
343111.5%
 
50510511.5%
 
598611.5%
 
7448411.5%
 
716511.5%
 
19505511.5%
 
55349111.5%
 
4648411.5%
 
456911.5%
 
4958511.5%
 
2007511.5%
 
16592311.5%
 
1106511.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
216411.5%
 
232211.5%
 
343111.5%
 
346211.5%
 
367011.5%
 
379511.5%
 
415811.5%
 
456911.5%
 
479511.5%
 
480811.5%
 
ValueCountFrequency (%) 
55349111.5%
 
50510511.5%
 
39723111.5%
 
37621811.5%
 
30819011.5%
 
25394311.5%
 
23106111.5%
 
19505511.5%
 
16592311.5%
 
16011811.5%
 

target
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count67
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43341.20895522388
Minimum788.0
Maximum386518.0
Zeros0
Zeros (%)0.0%
Memory size664.0 B
2020-08-25T00:02:29.525617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum788
5-th percentile1509.4
Q13055
median14152
Q345974
95-th percentile189736.3
Maximum386518
Range385730
Interquartile range (IQR)42919

Descriptive statistics

Standard deviation74833.26272
Coefficient of variation (CV)1.726607645
Kurtosis9.624875457
Mean43341.20896
Median Absolute Deviation (MAD)12110
Skewness2.992302
Sum2903861
Variance5600017210
2020-08-25T00:02:29.635369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
32870211.5%
 
1948211.5%
 
9731811.5%
 
38651811.5%
 
383511.5%
 
2817711.5%
 
956511.5%
 
19966011.5%
 
1279511.5%
 
323911.5%
 
26894511.5%
 
10768011.5%
 
2964111.5%
 
4155911.5%
 
7356011.5%
 
2550111.5%
 
3264411.5%
 
4916911.5%
 
6142511.5%
 
279611.5%
 
139911.5%
 
408411.5%
 
695211.5%
 
101111.5%
 
303811.5%
 
Other values (42)4262.7%
 
ValueCountFrequency (%) 
78811.5%
 
101111.5%
 
139911.5%
 
142011.5%
 
171811.5%
 
182511.5%
 
191011.5%
 
204211.5%
 
215411.5%
 
215511.5%
 
ValueCountFrequency (%) 
38651811.5%
 
32870211.5%
 
26894511.5%
 
19966011.5%
 
16658111.5%
 
14011511.5%
 
10768011.5%
 
9731811.5%
 
9706311.5%
 
7497711.5%
 

Interactions

2020-08-25T00:01:56.206514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:56.331725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:56.485828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:56.669010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:56.807589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:56.923450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.040766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.162500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.458154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.590722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.714130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.836462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:57.964707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.095845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.218796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.349390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.478799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.611379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.739853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.866236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:58.987586image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.109700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.234999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.365223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.496659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.626203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.760242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:01:59.885579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.023911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.148319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.285139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.404241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.529787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.642627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.758405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:00.867265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.160050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.276731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.400056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.526243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.643836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.760911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.869859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:01.996454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.110202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.236891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.357646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.483364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.601750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.726893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.839899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:02.962949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.094411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.220483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.343229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.466327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.589323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.703234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.832562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:03.949951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.083775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.196796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.312475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.420164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.536243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.822832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:04.936603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.052469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.166207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.277397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.389367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.513553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.623908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.747202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.859898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:05.985240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.110555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.236205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.353060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.470555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.584126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.699500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.817333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:06.938595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.059210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.182907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.300545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.410956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.537765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.650696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.774513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:07.898111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.024487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.142005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.429606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.543319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.663278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.784277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:08.908271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.030203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.158126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.283218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.398781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.534214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.661112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.793122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:09.921793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:10.050832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:10.176662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:10.311563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:10.429019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:10.551220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:02:11.567398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:11.704265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:11.826197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:12.120149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:02:12.842412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:02:13.087095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.213963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.327941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.458211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.578761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.713990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.837553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:13.965868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.084687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.209804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.325641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.447985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.569724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.699036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.821702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:14.944524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.071749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.191364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.325787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.446636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.582987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:15.896154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.022728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.140674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.266363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.380936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.499498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.622927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.751859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.874335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:16.997684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.121035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.241855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.371643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.493470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.624178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.739155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.854303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:17.961137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.072469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.175194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.297312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.412140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.526201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.644833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.758248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.870125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:18.973782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:19.096482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:19.205927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:19.506388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:19.640039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:19.787251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:02:20.176391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:20.308192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:20.442856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:20.584755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:20.718508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:20.859987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.003430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.134886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.292815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.426560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.567266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.686148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.807318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:21.923152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.041142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.150983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.264842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.386656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.514231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.635375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.755507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.880072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:22.991611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:23.304906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:23.422086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:23.549197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:23.684805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T00:02:23.957111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.088179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.218001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.351860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.485412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.623092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.761236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:24.896618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:25.030202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:25.155749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:25.297912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:25.428528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:02:29.777097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:02:30.213561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:02:30.501846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:02:30.781235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:02:25.705623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:02:26.044914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Bush00Buchanan00Nader00Browne00Hagelin00Harris00McReynolds00Moorehead00Phillips00Total00Clinton96Dole96Perot96Total96target
034062.0262.03215.0658.042.04.0658.021.020.086242.040144.025303.08072.074484.047300.0
15610.073.053.017.03.00.00.03.03.08154.02273.03684.0667.06634.02392.0
238637.0248.0828.0171.018.05.03.037.018.058815.017020.028290.05922.051566.018850.0
35413.065.084.028.02.00.00.03.02.08669.03356.04038.0819.08247.03072.0
4115185.0570.04470.0643.039.011.011.076.072.0218395.080416.087980.025249.0195055.097318.0
5177279.0789.07099.01212.0128.049.035.0123.074.0573306.0320736.0142834.038964.0505105.0386518.0
62873.090.039.010.01.00.01.03.02.05174.01794.01717.0630.04158.02155.0
735419.0182.01461.0127.015.06.03.012.019.066885.027121.027836.07783.063014.029641.0
829744.0270.01378.0194.016.05.00.028.018.057154.022042.020114.07244.049585.025501.0
941745.0186.0562.0204.014.01.03.09.06.057360.013246.030332.03281.047035.014630.0

Last rows

Bush00Buchanan00Nader00Browne00Hagelin00Harris00McReynolds00Moorehead00Phillips00Total00Clinton96Dole96Perot96Total96target
5783100.0305.04066.0431.094.011.05.059.015.0160940.016713.027311.04205.048539.072854.0
5875293.0194.01940.0551.038.038.05.070.027.0137044.036168.028892.08482.073897.058888.0
5912126.0114.0307.053.02.02.00.017.03.022258.07014.05960.02375.015397.09634.0
608014.0108.0182.053.04.02.00.05.09.012461.04479.05742.01874.012144.04084.0
614051.027.059.04.03.00.01.01.08.06801.03583.03188.01140.07997.02647.0
622326.026.029.013.00.01.00.00.00.03794.01388.01636.0425.03462.01399.0
6382214.0396.02436.03211.033.09888.03.059.02927.0198230.078905.063067.017319.0160118.097063.0
644511.046.0149.030.03.02.01.06.00.08583.03054.02931.01091.07165.03835.0
6512176.0120.0265.068.011.03.02.018.07.018307.05341.07706.02342.015514.05637.0
664983.088.093.032.020.00.00.05.09.08026.02992.03522.01287.07859.02796.0